Double DQN (Double Deep Q-Network) improves Q-learning by addressing the issue of overestimation bias found in traditional Q-learning methods. In standard Q-learning, the technique uses the same values to both select and evaluate actions, which can lead to an overestimation of action values. This means that the algorithm may prefer actions that are not necessarily the best due to inaccuracies in value estimation. Double DQN separates the action selection and evaluation processes, which enhances the overall learning stability and efficiency.
The way Double DQN works is fairly straightforward. It utilizes two neural networks: one that is responsible for selecting the action (the online network) and another that evaluates the action's value (the target network). When the agent makes a decision about which action to take, it uses the online network to predict the action value. However, once that action is selected, the target network computes the value of the selected action at that next state, which helps to reduce the overestimation that happens when both tasks use the same network. By doing this, Double DQN provides a more accurate assessment of the expected rewards for each action.
For example, let’s say you are training an agent to play a game like Atari’s Breakout. In standard DQN, if the agent frequently overestimates the value of hitting a ball towards one side of the screen, it may favor that action even if it often results in losing the ball. With Double DQN, the agent would choose an action based on the online network but would then calculate its value using the target network, thereby receiving a more reliable evaluation. This refined approach allows for better learning and improves the agent's overall performance in making optimal decisions.